In this paper one investigates the merit of partial user annotation for music transcription using a PLCA-based model. The original algorithm, called Blind Harmonic Adaptive Decomposition (BHAD), provides an estimation of the polyphonic pitch content of the input signal in an entirely unsupervised manner. In this paper, one studies how the performance of the BHAD algorithm can be further improved by involving a user by means of a partial annotation. This user input allows for a better model initialisation with adapted or learned spectral envelope models. Furthermore, it is studied how a fine control of the convergence rate of some parameters can better exploit this additional information. It is then shown that this partial annotation can bring an improvement of up to 3% on the transcription of the remaining file.